Abstract
We present Annealed Mutational approximated Landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiments sequencing data. Such experiments typically start from a single wild-type sequence, which undergoes Darwinian in vitro evolution via multiple rounds of mutation and selection for a target phenotype. In the last years, Directed Evolution is emerging as a powerful instrument to probe fitness landscapes under controlled experimental conditions and as a relevant testing ground to develop accurate statistical models and inference algorithms (thanks to high-throughput screening and sequencing). Fitness landscape modeling either uses the enrichment of variants abundances as input, thus requiring the observation of the same variants at different rounds or assuming the last sequenced round as being sampled from an equilibrium distribution. AMaLa aims at effectively leveraging the information encoded in the whole time evolution. To do so, while assuming statistical sampling independence between sequenced rounds, the possible trajectories in sequence space are gauged with a time-dependent statistical weight consisting of two contributions: (i) an energy term accounting for the selection process and (ii) a generalized Jukes–Cantor model for the purely mutational step. This simple scheme enables accurately describing the Directed Evolution dynamics and inferring a fitness landscape that correctly reproduces the measures of the phenotype under selection (e.g., antibiotic drug resistance), notably outperforming widely used inference strategies. In addition, we assess the reliability of AMaLa by showing how the inferred statistical model could be used to predict relevant structural properties of the wild-type sequence.
Highlights
IntroductionOver the last few years, the development of increasingly accurate high-throughput biochemical assays with massive parallel sequencing techniques has established largescale genetic screening as a fundamental tool for the investigation of the relationship between evolution, fitness and other important biological concepts that were behind the experimental research [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]
We propose Annealed Mutational approximated Landscape analysis (AMaLa), a new unsupervised inference framework that effectively takes into account the mutational aspects of Directed Evolution experiments in terms of a global likelihood to observe a time series of variants’ abundances
The experiment alternates rounds of variants selection and mutagenesis steps where part of the population is randomly mutated through error-prone PCR
Summary
Over the last few years, the development of increasingly accurate high-throughput biochemical assays with massive parallel sequencing techniques has established largescale genetic screening as a fundamental tool for the investigation of the relationship between evolution, fitness and other important biological concepts that were behind the experimental research [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27] These experiments that simultaneously screen up to millions of variants of a given protein come in two main flavors. At some of the intermediate steps, a sample of the mutant population is sequenced to assess the relative variant abundances
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